Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024). Article CAS PubMed PubMed Central Google Scholar Qian, L., Dong, Z. & Guo, T. Grow AI virtual cells: three data pillars and closed-loop learning. Cell Res. 35, 319–321 (2025). Article PubMed PubMed Central Google Scholar
Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024).
Google Scholar
Qian, L., Dong, Z. & Guo, T. Grow AI virtual cells: three data pillars and closed-loop learning. Cell Res. 35, 319–321 (2025).
Google Scholar
Cui, H. et al. Towards multimodal foundation models in molecular cell biology. Nature 640, 623–633 (2025).
Google Scholar
Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).
Google Scholar
Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481–1491 (2024).
Google Scholar
Rosen, Y. R. et al. Universal cell embeddings: a foundation model for cell biology. Nature 656, 183–191 (2026).
Abhinav K. et al. Predicting cellular responses to perturbation across diverse contexts with state. Preprint at bioRxiv https://doi.org/10.1101/2025.06.26.661135 (2025).
Dong, M. et al. Stack: in-context learning of single-cell biology. Preprint at bioRxiv https://doi.org/10.64898/2026.01.09.698608 (2026).
Wang, C. et al. X-Cell: scaling causal perturbation prediction across diverse cellular contexts via diffusion language models. Preprint at bioRxiv https://doi.org/10.64898/2026.03.18.712807 (2026).
Bunne, C. et al. Learning single-cell perturbation responses using neural optimal transport. Nat. Methods 20, 1759–1768 (2023).
Google Scholar
Yeo, G. H. T., Saksena, S. D. & Gifford, D. K. Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nat. Commun. 12, 3222 (2021).
Google Scholar
Tong, A., Huang, J., Wolf, G., van Dijk, D. & Krishnaswamy, S. TrajectoryNet: a dynamic optimal transport network for modeling cellular dynamics. Proc. Mach. Learn. Res. 119, 9526–9536 (2020).
Google Scholar
Zhang, Z., Li, T. & Zhou, P. Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport. In International Conference on Learning Representations (ICLR, 2025).
Zhang, Z. et al. Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data. npj Syst. Biol. Appl. 12, 2 (2026).
Google Scholar
Boiarsky, R. et al. Deeper evaluation of a single-cell foundation model. Nat. Mach. Intell. 6, 1443–1446 (2024).
Google Scholar
Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Zero-shot evaluation reveals limitations of single-cell foundation models. Genome Biol. 26, 101 (2025).
Google Scholar
Ahlmann-Eltze, C., Huber, W. & Anders, S. Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Nat. Methods 22, 1657–1661 (2025).
Google Scholar
Gillet, L. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 https://doi.org/10.1074/mcp.O111.016717 (2012).
Qian, L. et al. AI-empowered perturbation proteomics for complex biological systems. Cell Genom. 4, 100691 (2024).
Google Scholar
Xiao, Q. et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv. Drug Deliv. Rev. 176, 113844 (2021).
Google Scholar
Guo, T., Steen, J. A. & Mann, M. Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature 638, 901–911 (2025).
Google Scholar
Chen, R. T., Rubanova, Y., Bettencourt, J. & Duvenaud, D. K. Neural ordinary differential equations. In Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio, S. et al.) 6571–6583 (Curran Associates, 2018).
Weinan, E. A proposal on machine learning via dynamical systems. Comm. Math. Stat. 5, 1–11 (2017).
Google Scholar
Jaaks, P. et al. Effective drug combinations in breast, colon and pancreatic cancer cells. Nature 603, 166–173 (2022).
Google Scholar
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Google Scholar
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles Cell 171, 1437–1452 (2017).
Google Scholar
Li, X. et al. LncRNA NEAT1 promotes autophagy via regulating miR-204/ATG3 and enhanced cell resistance to sorafenib in hepatocellular carcinoma. J. Cell. Physiol. 235, 3402–3413 (2020).
Google Scholar
Hu, J. et al. BTF3 sustains cancer stem-like phenotype of prostate cancer via stabilization of BMI1. J. Exp. Clin. Cancer Res. 38, 227 (2019).
Google Scholar
Phi, L. T. H. et al. Cancer stem cells (CSCs) in drug resistance and their therapeutic implications in cancer treatment. Stem Cells Int. 2018, 5416923 (2018).
Google Scholar
De Greve, J. & Giron, P. Targeting the tyrosine kinase inhibitor-resistant mutant EGFR pathway in lung cancer without targeting EGFR? Transl. Lung Cancer Res. 9, 1–3 (2020).
Google Scholar
Liu, L. et al. The LIS1/NDE1 complex is essential for FGF signaling by regulating FGF receptor intracellular trafficking. Cell Rep. 22, 3277–3291 (2018).
Google Scholar
Park, G. B., Jeong, J. Y., Choi, S., Yoon, Y. S. & Kim, D. Glucose deprivation enhances resistance to paclitaxel via ELAVL2/4-mediated modification of glycolysis in ovarian cancer cells. Anticancer Drugs 33, e370–e380 (2022).
Google Scholar
Yang, T. et al. CKS2 promotes the malignant phenotypes of bladder cancer cells via PI3K/AKT signaling pathway activation. Cell Cycle 24, 687–701 (2025).
Google Scholar
Yang, X. et al. GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model. Cell Res. 34, 830–845 (2024).
Google Scholar
Preuer, K. et al. DeepSynergy: predicting anti-cancer drug synergy with deep learning. Bioinformatics 34, 1538–1546 (2018).
Google Scholar
Konstantinopoulos, P. A. et al. A phase II, two-stage study of letrozole and abemaciclib in estrogen receptor-positive recurrent endometrial cancer. J. Clin. Oncol. 41, 599–608 (2023).
Google Scholar
Ingham, M. et al. Phase II study of olaparib and temozolomide for advanced uterine leiomyosarcoma (NCI Protocol 10250). J. Clin. Oncol. 41, 4154–4163 (2023).
Google Scholar
Farago, A. F. et al. Combination olaparib and temozolomide in relapsed small-cell lung cancer. Cancer Discov. 9, 1372–1387 (2019).
Google Scholar
Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274 (2016).
Google Scholar
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).
Google Scholar
Wang, J. et al. CDK7 inhibitor THZ1 enhances antiPD-1 therapy efficacy via the p38alpha/MYC/PD-L1 signaling in non-small cell lung cancer. J. Hematol. Oncol. 13, 99 (2020).
Google Scholar
Wang, Z. et al. HDAC6 promotes cell proliferation and confers resistance to gefitinib in lung adenocarcinoma. Oncol. Rep. 36, 589–597 (2016).
Google Scholar
Zecha, J. et al. Decrypting drug actions and protein modifications by dose- and time-resolved proteomics. Science 380, 93–101 (2023).
Google Scholar
Eckert, S. et al. Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics. Nat. Biotechnol. 43, 406–415 (2025).
Ruprecht, B. et al. A mass spectrometry-based proteome map of drug action in lung cancer cell lines. Nat. Chem. Biol. 16, 1111–1119 (2020).
Google Scholar
Dibaeinia, P. et al. Virtual cells need context, not just scale. Preprint at bioRxiv https://doi.org/10.64898/2026.02.04.703804 (2026).
Liu, Z. et al. DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis. Nat. Commun. 16, 3530 (2025).
Google Scholar
Wallmann, G. et al. AlphaDIA enables DIA transfer learning for feature-free proteomics. Nat. Biotechnol. 44, 1168–1177 (2026).
Tang, X. et al. CellForge: agentic design of virtual cell models. Preprint at https://doi.org/10.48550/arXiv.2508.02276 (2025).
Mitchell, D. C. et al. A proteome-wide atlas of drug mechanism of action. Nat. Biotechnol. 41, 845–857 (2023).
Google Scholar
Cai, X. et al. High-throughput proteomic sample preparation using pressure cycling technology. Nat. Protoc. 17, 2307–2325 (2022).
Google Scholar
Sun, R. et al. Accelerated protein biomarker discovery from FFPE tissue samples using single-shot, short gradient microflow SWATH MS. J. Proteome Res. 19, 2732–2741 (2020).
Google Scholar
Sun, R. et al. A prostate cancer tissue specific spectral library for targeted proteomic analysis. Proteomics 22, e2100147 (2022).
Google Scholar
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
Google Scholar
Zhong, Q. et al. Proteomic-based stratification of intermediate-risk prostate cancer patients. Life Sci. Alliance 7, e202302146 (2024).
Sun, R. et al. Proteomic dynamics of breast cancer cell lines identifies potential therapeutic protein targets. Mol. Cell Proteomics 22, 100602 (2023).
Google Scholar
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Google Scholar
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Google Scholar
Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).
Google Scholar
Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).
Google Scholar
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 4768–4777 (Curran Associates Inc., 2017).
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